1. Technical Field
The present invention relates to detecting shapes in images, and more particularly, to a system and method for the detection of shapes, such as polyps and diverticuloses, in one or more images acquired for a virtual colonoscopy.
2. Discussion of the Related Art
In the field of medical imaging, various systems have been developed for generating medical images of various anatomical structures of individuals for the purposes of screening and evaluating medical conditions. These imaging systems include, for example, computed tomography (CT) imaging, magnetic resonance imaging (MRI), positron emission tomography (PET), etc. Each imaging modality may provide unique advantages over other modalities for screening and evaluating certain types of diseases, medical conditions or anatomical abnormalities, including, for example, colonic polyps, aneurysms, lung nodules, calcification on heart or artery tissue, cancer micro-calcifications or masses in breast tissue, and various other lesions or abnormalities.
For example, CT imaging systems can be used to obtain a set of cross-sectional images or two-dimensional (2D) “slices” of a region or interest (ROI) of a patient for purposes of imaging organs and other anatomies. The CT modality is commonly employed for purposes of diagnosing disease because such a modality provides precise images that illustrate the size, shape, and location of various anatomical structures such as organs, soft tissues, and bones, and enables a more accurate evaluation of lesions and abnormal anatomical structures such as cancer, polyps, etc.
One method that physicians, clinicians, radiologists, etc., use for diagnosing and evaluating medical conditions is to manually review hard-copies (X-ray films, prints, photographs, etc.) of medical images that are reconstructed from an acquired dataset, to discern characteristic features of interest. For example, CT image data that is acquired during a CT examination can be used to produce a set of 2D medical images (X-ray films) that can be viewed to identify potential abnormal anatomical structures or lesions by a trained physician, clinician, radiologist, etc. In another method, a virtual colonoscopy is used to review medical images that include normal anatomical structures corresponding to the colon. A trained radiologist, for example, may be able to identify small polyps among these structures that are potentially cancerous or pre-cancerous. However, the trained radiologist may overlook a medical condition such as colonic polyps due to human error.
Accordingly, various image processing systems and tools have been developed to assist physicians, clinicians, radiologists, etc. in evaluating medical images to diagnose medical conditions. For example, computer-aided detection (CAD) tools have been developed for various clinical applications to provide automated detection of medical conditions in medical images. In general, CAD systems employ methods for digital signal processing of image data (e.g., CT data) to automatically detect colonic polyps and other abnormal anatomical structures such as lung nodules, lesions, aneurysms, calcification on heart or artery tissue, micro-calcifications or masses in breast tissue, etc.
Although such CAD systems are useful for diagnostic purposes, they typically rely on expensive procedures associated with candidate (e.g., colonic polyps and/or diverticuloses) generation. In addition, they suffer from high false positives (e.g., incorrectly identifying normal tissues as abnormal) and false negatives (e.g., failing to correctly identify abnormalities).
Accordingly, there is a need for CAD a system and method that accurately detects shapes associated with medical conditions such as colonic polyps in medical images to reduce the amount of false positives and/or false negatives.